{"id":"https://openalex.org/W3094401034","doi":"https://doi.org/10.1109/icovet50258.2020.9229888","title":"Face Recognition System using Facenet Algorithm for Employee Presence","display_name":"Face Recognition System using Facenet Algorithm for Employee Presence","publication_year":2020,"publication_date":"2020-09-19","ids":{"openalex":"https://openalex.org/W3094401034","doi":"https://doi.org/10.1109/icovet50258.2020.9229888","mag":"3094401034"},"language":"en","primary_location":{"id":"doi:10.1109/icovet50258.2020.9229888","doi":"https://doi.org/10.1109/icovet50258.2020.9229888","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icovet50258.2020.9229888","pdf_url":null,"source":{"id":"https://openalex.org/S7407088204","display_name":"International Conference on Vocational Education and Training (ICOVET)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 4th International Conference on Vocational Education and Training (ICOVET)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5044691389","display_name":"Ferry Budi Cahyono","orcid":"https://orcid.org/0000-0003-2792-2665","observed_orcids":["https://orcid.org/0000-0003-2792-2665"]},"institutions":[{"id":"https://openalex.org/I166843116","display_name":"Institut Teknologi Sepuluh Nopember","ror":"https://ror.org/05kbmmt89","country_code":"ID","type":"education","lineage":["https://openalex.org/I166843116"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Ferry Cahyono","raw_affiliation_strings":["Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia","institution_ids":["https://openalex.org/I166843116"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114930148","display_name":"Wirawan Wirawan","orcid":"https://orcid.org/0000-0003-0783-3710","observed_orcids":["https://orcid.org/0000-0003-0783-3710"]},"institutions":[{"id":"https://openalex.org/I166843116","display_name":"Institut Teknologi Sepuluh Nopember","ror":"https://ror.org/05kbmmt89","country_code":"ID","type":"education","lineage":["https://openalex.org/I166843116"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Wirawan Wirawan","raw_affiliation_strings":["Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia","institution_ids":["https://openalex.org/I166843116"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084212006","display_name":"Reza Fuad Rachmadi","orcid":"https://orcid.org/0000-0001-9101-5598","observed_orcids":["https://orcid.org/0000-0001-9101-5598"]},"institutions":[{"id":"https://openalex.org/I166843116","display_name":"Institut Teknologi Sepuluh Nopember","ror":"https://ror.org/05kbmmt89","country_code":"ID","type":"education","lineage":["https://openalex.org/I166843116"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Reza Fuad Rachmadi","raw_affiliation_strings":["Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia","institution_ids":["https://openalex.org/I166843116"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I166843116"],"apc_list":null,"apc_paid":null,"fwci":2.005,"has_fulltext":false,"cited_by_count":50,"citation_normalized_percentile":{"value":0.91018049,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":93,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"57","last_page":"62"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.9818999767303467,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.9818999767303467,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.016300000250339508,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10828","display_name":"Biometric Identification and Security","score":0.0010000000474974513,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/face-recognition","display_name":"face recognition","score":0.9950000047683716},{"id":"https://openalex.org/keywords/facenet","display_name":"FaceNet","score":0.9340000152587891},{"id":"https://openalex.org/keywords/openface","display_name":"OpenFace","score":0.9869999885559082},{"id":"https://openalex.org/keywords/employee-attendance-system","display_name":"employee attendance system","score":0.7139999866485596},{"id":"https://openalex.org/keywords/facial-feature-extraction","display_name":"facial feature extraction","score":0.7300000190734863},{"id":"https://openalex.org/keywords/support-vector-machine-classification","display_name":"support vector machine classification","score":0.875},{"id":"https://openalex.org/keywords/model-comparison","display_name":"model comparison","score":0.38199999928474426}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7646815776824951},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.7239592671394348},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.7069073915481567},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6789810061454773},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6640796661376953},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.6236497759819031},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5101590752601624},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32028231024742126},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icovet50258.2020.9229888","doi":"https://doi.org/10.1109/icovet50258.2020.9229888","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icovet50258.2020.9229888","pdf_url":null,"source":{"id":"https://openalex.org/S7407088204","display_name":"International Conference on Vocational Education and Training (ICOVET)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 4th International Conference on Vocational Education and Training (ICOVET)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W341112413","https://openalex.org/W429766147","https://openalex.org/W1998808035","https://openalex.org/W2080282247","https://openalex.org/W2096733369","https://openalex.org/W2138451337","https://openalex.org/W2145287260","https://openalex.org/W2339037049","https://openalex.org/W2487770199","https://openalex.org/W2513140567","https://openalex.org/W2797558164","https://openalex.org/W2910603373","https://openalex.org/W2992532478","https://openalex.org/W3041378004","https://openalex.org/W4250664506"],"related_works":["https://openalex.org/W3093531064","https://openalex.org/W4320057779","https://openalex.org/W7171784368","https://openalex.org/W4402553035","https://openalex.org/W4411714996","https://openalex.org/W4312655519","https://openalex.org/W3165886424","https://openalex.org/W3006346533","https://openalex.org/W4386901419","https://openalex.org/W4387982492"],"abstract_inverted_index":{"This":[0,66],"study":[1],"discusses":[2],"the":[3,37,71,79,81,92,102,107,112,126,154,157,162,166,173],"appropriate":[4],"method":[5],"to":[6,64,121],"be":[7,85,118],"applied":[8],"in":[9,40],"a":[10,29,46],"presence":[11],"system":[12],"using":[13,101,153,172],"faces":[14],"by":[15,32,69,87],"comparing":[16],"two":[17],"deep":[18],"learning":[19],"architectural":[20],"models,":[21],"they":[22],"are":[23,131,138],"FaceNet":[24,27,49,103,133],"and":[25,90,104],"Openface.":[26,105],"is":[28,45,51,61,144,170],"model":[30,155,167],"developed":[31],"Google":[33],"researchers":[34],"that":[35,50,60,136,143,169],"has":[36,57,161],"highest":[38,158],"accuracy":[39,59,134,142,159],"face":[41,73,82],"recognition.":[42],"While":[43],"Openface":[44,147],"development":[47],"from":[48],"trained":[52],"with":[53,140,156],"smaller":[54],"datasets":[55],"but":[56],"an":[58,75],"almost":[62],"equal":[63],"FaceNet.":[65],"will":[67,84,117],"start":[68],"taking":[70],"employee's":[72],"into":[74,98],"image":[76],"dataset.":[77],"From":[78],"dataset,":[80],"preprocessing":[83],"performed":[86],"detecting,":[88],"cropping,":[89],"resizing":[91],"face.":[93],"Then":[94],"extracting":[95],"facial":[96,115],"features":[97,116],"128":[99],"dimensions":[100],"With":[106],"Support":[108],"Vector":[109],"Machine":[110],"(SVM),":[111],"classification":[113],"of":[114,177],"carried":[119],"out":[120],"obtain":[122],"accuracy.":[123,150],"To":[124],"validate":[125],"model,":[127],"5":[128],"fold":[129],"cross-validations":[130],"used.":[132],"results":[135,164],"obtained":[137],"higher":[139],"perfect":[141],"100%,":[145],"while":[146],"only":[148],"93.33%":[149],"The":[151],"implementation":[152],"(FaceNet)":[160],"same":[163],"as":[165],"testing":[168],"100%":[171],"introduction":[174],"threshold":[175],"probability":[176],"0.25.":[178]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":17},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":11},{"year":2021,"cited_by_count":6}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2020-10-29T00:00:00"}
